Long-Context Mental Health Assessment with Large Language Models via Knowledge Compression

Assessing mental disorder causes and severity from social media history or clinical interviews is essential for early intervention and monitoring population mental health. However, existing methods face several challenges, especially under long-context: the inherently noisy and lengthy nature of online content obscures crucial clinical signals, while the computational intensity of Transformer-based models limits practical deployment. To address these issues, this paper proposes MindSpan, a novel framework that enhances automated mental health profiling by knowledge-augmented compression with output-controlled generation. Specifically, MindSpan first compresses the input long-context by actively retrieving and retaining the top symptom-relevant snippets, effectively filtering out irrelevant information. Then it applies a profiling-specific generation template that constrains the Large Language Models (LLMs) to produce structured, consistent, and clinically interpretable severity ratings. Experiments on two public benchmarks demonstrate that MindSpan achieves better performance. This work provides a practical and efficient solution for scalable, automated mental health assessment